Invest Like the Best with Patrick O'Shaughnessy - Michael Mauboussin - Man + Machine, Moats, and Power of the Outside View - [Invest Like the Best, EP.37]

Episode Date: May 16, 2017

My guest today is Michael Mauboussin, who is the head of global financial strategies at Credit Suisse and is on my short list of must read writers on all things investing. If you read his entire catalogue, Howard Marks's memos, and Buffett's shareholder letters, you be sitting pretty. Michael was also a big reason for the early success of this show appearing as my second guest and now my 37th. He and his team have been prolific in the last six months, publishing several long research reports on the most interesting aspects of the investing landscape. In this conversation, we talk about business moats, industry analysis, and how to combine man and machine when building an investment strategy and portfolio. As I tell Michael at the end, you won't be able to listen to this episode at two times speed, because we go deep quickly. For comprehensive show notes on this episode go to http://investorfieldguide.com/michael For more episodes go to InvestorFieldGuide.com/podcast. Sign up for the book club, where you’ll get a full investor curriculum and then 3-4 suggestions every month at InvestorFieldGuide.com/bookclub. Follow Patrick on Twitter at @patrick_oshag

This podcast is sponsored by CFA Institute, the Global Association of Investment Professionals whose mission is to lead the investment profession by promoting the highest standards of ethics, education, and professional excellence for the ultimate benefit of society. CFA Institute serves a global community of investment professionals, working to build an investment industry where investors' interests come first, financial markets function at their best, and economies grow. The chartered financial analyst credential is the most respected and recognized investment management designation in the world.

The views expressed in this podcast do not necessarily represent the views of CFA Institute. Hello and welcome, everyone. I'm Patrick O'Shaughnessy and this is Invest Like the Best. This show is an open-ended exploration of markets, ideas, methods, stories, and of strategies that will help you better invest both your time and your money. You can learn more and stay up to date at investorfieldguide.com. Patrick O'Shaunisee is a principal and portfolio manager at O'Shaunicee Asset Management. opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect

the opinion of Oshamously Asset Management. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of Oshonnessy Asset Management may maintain positions in the securities discussed in this podcast. My guest today is Michael Mobison, who is the head of global financial strategies at Credit Swiss and on my short list of must-read writers on all things investing. If you just read his entire catalog, Howard marks his memos, and Buffett's shareholder letters, you'd be sitting pretty. He was also a big reason for the early success of this show, appearing as my second guest and now as my 37. Michael and his team have been prolific in the last six months, publishing several long research reports on the most interesting aspects of the investing landscape.

In this conversation, we talk about business moats, industry analysis, and how to combine man and machine when building an investment strategy and portfolio. as I tell Michael at the end, you won't be able to listen to this episode at two-time speed because we go deep quickly. You can find show notes for this episode at investorfieldguide.com forward slash Michael. And now, please enjoy my conversation with Michael Mobeson. Well, Michael, thanks for coming back yet again. We're going to have a lot of fun discussing, I think five or six of your recent papers, which is kind of mind-bending how much work you put out since six months ago when we first talk.

I know you've got great support and help, but it's been a fascinating collection of papers on things like the active passive debate, the declining propensity of companies to list their shares, what is a moat, how might you find one, how much should you pay for one? All these things will be really interesting to talk about. But we were having a conversation, and I figured I just hit record, and we'll start with this active passive debate. And really the two, I've heard kind of two big outstanding questions, which is, where is the point of equilibrium? We need some active management. Obviously, the trend is more and more passive, kind of what is that point? No one seems to agree on that. And then the second point is

that I keep hearing, yes, everyone's going passive and it's cheaper, which is great, but behavior gaps aren't going to go away. So even if the S&P is up, you know, 10%, and now people are really getting that 10% instead of 8% because they're not paying the high active fees, very often if the time weighted return is 10, the investors return is 5. So who's getting that behavior gap? Who's earning that is it companies doing buybacks, both interesting areas to explore. So let's start with what you're telling me before we hit record, which is this really intriguing Berkin Green model for kind of the active passive debate and we'll start to kind of flesh these questions out. Well, first of all, Patrick, great to be with you. And these are really fascinating. Of course, I think

really important to topics. Let me say that I was very low to go into this active passive debate at all, in part because there have been so much ink spilled on the topic and hard to know what to add. But I got to say that what encouraged me was that a lot of the discussion that was very superficial, candidly, and a lot of the discussion took on tones of religious fervor, right? Whether you're on the active side or the passive side, people seem to think there is no other solution to this. So I try to write this piece. We spent a lot of time reading academic research on this, and we wrote the piece, and I have to say that my, what I really did want to write, Patrick, I wanted to write a piece that said, the equilibrium is 72.4% right, like some sort of magic number.

and of course that that completely alluded me. But I was very motivated by this paper on the impossibility of informationally efficient markets, which was written by Sandy Grossman and Joe Stiglitz back in 1980. And the basic idea is very straightforward and I think economically quite sound. They said markets cannot be perfectly efficient because there's a cost to gathering information and reflecting in prices. And as a compensation for that cost, there should be a requisite benefit in the form of inefficient markets. So Professor Lasse-Petterson's got this phrase, which I love, called,

markets have to be efficiently inefficient, enough inefficiency to get people to keep doing this, but not so efficient that there are a lot of money laying around. So that model, Grossman-Sticklets, I thought, was always for me very motivating. So now the question is, how much available excess return is there? So how does one think about that? So you mentioned a moment ago, the model by Jonathan Burke and Richard Green, and I think that's a really nice way to approach that. So these guys said, you know, here's one of ways to think about this. By the way, usually when we give data on mutual fund performance will say X percent outperform the market or the standard deviation or performance some number. But the challenge is every fund is the same. You know, whether you're

a hundred million, a billion or a hundred billion, you're like counted as one datum. And as we know, what we really need is to have it all asset weighted for the most part. So what these guys said was here's a way to think about this is to evaluate fund performance just as you would evaluate firm performance. So if we want to know if a company is earning excess returns, we usually do something called an economic profit calculation, right? We look at return on investment capital, less cost of capital, then we look at that spread, times invest the capital, right? And if that number is positive, it means they're creating value. If it's negative, they're destroying value. These guys said that's what we should do for funds. So what we're going to do is look at

gross returns. So this is pre-fee minus benchmark return. Of course, you can make a risk adjustment in there if that's appropriate, times AUM. And what that ends up being is the amount of value that you can extract from the market. And so as you start to think about this, right, because you can have small spreads, big AUM, big spread, small, UM, and so forth. And they give a really nice example in the paper. They said, Peter Lynch, when he's going in the mid-70s for Magelland, he was lighting it up, had monster alpha, but was running a relatively modest amount of money. So actually his extraction from the market was not that big. So he's playing poker at the nickel and dime table. He's cleaning up, but there's not that much money to May. By the end, of course,

AOM flows to him, so the fund grows a lot. And so to the end, he still had positive alpha, but much more modest, but on a much bigger AUM base. So he's actually extracting a lot more dollars from the markets. And now he's playing with the big boys, the big boy poker table. He's still winning, not the same magnitude, but the dollar sums are much bigger. So what we've been able to look at since the mid-1970s is to look at those aggregate economic profit numbers to get a sense, again, how much money is available to win or to lose. And I think that's a really nice way to start to approach this problem.

Now, the simple characterization of active to passive to me is that the amount of available alpha over the last, for sure, 10, 15 years has declined faster than the fees what people are willing to pay going back to our grossman stiglitz. So we're in this sort of natural rebalancing phase where the fees are going to come down to get to some sort of equilibrium. So it doesn't go forever, obviously. not everyone can be passive or ETF owners, but that process has been strongly in place. And I think there are other factors as well, things like the Department of Labor Rule,

whether or not that got, it didn't get implemented, obviously in April, but there was a big pull-forward effect and so forth. So there are some others, you know, we'll call that more cyclical factors as well. But, yeah, so that's a really interesting framework for thinking about it. And that's sort of how we're going to try to kind of try to animate that point and see if we can get get our arms around it better. In the paper you published, you cited kind of four, looking back much further than 10 or 15 years, kind of four key drivers of this whole movement, which is regulation.

You've touched on DOL as an example. Market environment, technology, and sort of the balance between, to use your poker analogy, again, like the balance between informed and uninformed sellers. So maybe just kind of flesh out that framework of how those four variables have changed our industry, kind of going back to maybe the 70s. Yeah, exactly. So the regulation one's the first one. I was encouraged by one of my colleagues to go back.

So we actually went back to 1930s on that. But you can trace a series of regulatory changes over time, some of which these were ostensibly intended and some were not intended, that really encouraged both the move from individuals into mutual funds in some way, shape, or form. And then later on from mutual fund generally to index funds. So that has been a real clear path that has promoted the growth in the industry in general and promoted the growth of index funds more recently. more specifically. So that's a big one. Technology, you know, Patrick, you step back and say that's just

a huge factor. And leaving aside all this other stuff we're talking about in terms of people going into the industry and so and so forth, it's just technology's mind-boggling, right? You just think about what you have at your disposal today in terms of your computer access to information, number crunching, and so forth. It's just remarkable relative to a fairly short period of time ago. I started in the industry just to shade over 30 years ago. And I was in a training program and we rotated through different parts of the firm, all my co-trainees. And I remember one of our trainees, we were all assigned to the research department. And one of my other trainees, colleagues, was actually working with an analyst. This is now 1986, where the analyst was still doing all his spreadsheets on paper. Like,

it was spreadsheet, not like literally on paper. So the analyst would like do some model. he's writing them all out right in this paper and he'd hand it to the senior analyst and he go no I think the gross margin should be 39% not 37% can you recalculate and the guy would have to erase everything and like recalculated by hand so mind-boggling that that's in my certainly my career so technologies are really a big component and another way to think about that is if I gave you what's at your fingertips today put you back in the 1960s or 70s you could run you could run circles around your competition right because you'd just be so much better equipped marketing environment's also super interesting. And this is, you mentioned going back to the 70s, but if you're

a historian of the markets, it's really interesting to see how people do change their behaviors, given the conditions. And the thing we talked about was in the 1970s, you go back to 72, we'd come off a very good period for the stock market. The money market funds world didn't exist. And in the subsequent from called 72 to 1980, a very difficult equity market, certainly for large capitalization stocks, and basically the equity mutual funds lost AUM. The market, I think, was up a tad, but they lost a UM, money markets went from a flat standstill to something like $75 billion. So there's history demonstrating that when market returns are weak, investors seek low-cost alternatives.

So that's probably an undertone, certainly in the wake, certainly in the financial crisis. And then as you pointed out, that goes back to the Grossman Stiglitz, you know, this weak game thing, which is how do we think about that balance between the uninformed and informed players? And, you know, that's the other thing that I hear from, by the way, money managers who I really respect I think are really smart will say something like the fact that everyone's going to indexes and ETFs makes our lives easier. And I'm like, I think at first blush that makes sense because they're not thinking. But on the second, on second blush, right, you say to yourself, well, if that used to be this

dumb money, in other words, those are the people I could out think or out analyze. And they're now basically throwing in the towel and they're going to earn the index return. That means the only people I'm left competing with are they're really smart people. So in a sense, it actually makes your job more difficult, not let. difficult. So it's this interesting idea that that that may be a contributing factor to this degree of competition. It's interesting how everyone's views on this have evolved. There's a great passage in one of the Buffett letters that says something like, you know, what better advantage could there be than a whole chunk of the markets that's opted out of the

competition? And of course, now his views are, you know, if you don't buy Berkshire, buy the S&P 500. I was at the Berkshire meeting for the first time and people were actually grilling him on why he said buy the S&P versus by Berkshire. And his answer was kind of wishy-wisey. It wasn't great. And your papers have shown kind of the declining, what is it, volatility of excess return or something like that, basically to show that it's getting relative skill is the thing that matters, not absolute skill.

So the fact that we've got these fancy spreadsheets doesn't really matter because everyone has them and knows kind of how to use them. Exactly. What do you think about distortions that are created by active? and the interesting question here is always timing. So maybe there are distortions, but maybe those distortions last longer than the active managers can stay in business. Well, let me say, your second question about this raised some really important issues. And we'll talk about the behavioral gap as well.

But I do think that the move into index funds and ETFs in particular have led to three things that are really important for investors to understand. Before I delve into those, by the way, I should just mention that when I talk to money managers, active managers, the single greatest point of frustration, I think, is a distinction that in behavioral economics, we distinguish between prices are right, which means markets are informationally efficient, and no free lunch, which says there is no strategy that reliably beats the market. Now, if prices are right, there is no free lunch, and I think that's quite straightforward for people to understand, but the inverse is not true. So in other words, it can be difficult to beat the market,

and markets can be not perfectly efficient. So a lot of money managers I hear are very frustrated because they see that there are mispricings out there, but they are actually very difficult to exploit. So why is that? And I think there are three specific things that indexing and ETFs have led to, and we'll see how this movie ends. First is valuations for certain stocks or certain asset classes are higher than they would otherwise be for not or for not for this trend. And as a parallel, I would go back. There's a really nice paper by Gomper's in Mexico. documenting the rise of large-cap stocks in the 80s and 90s. Right?

So it turns out the literature started, you know, late 70s, early 80s, that small caps outperform large-caps. So that's been known since at least that time. But if you had bought into that, right, and say 1980 and gone long, short small-cap stocks, you would have been wandering around the desert for two decades, right? Essentially. And so what these guys showed was that money flows when it, because it goes back to our regulations, money's monies were flowing into large mutual fund families, which themselves had an incentive to buy

large capitalization stocks, which themselves drove them up. And we went from very cheap large cap stocks in the early age to very dear, obviously at the peak of the 2000, right? So valuation higher than it would otherwise be. I think that's true. Second is intrastector correlations are going up, and that's really challenging. So now that just means that groups move together. And so you're now working at a hedge fund and you're a really smart guy and you're following the restaurant sector and you go along the cheap one and you go short the deer one and you love your positioning and investors decide we like restaurants and what do they do is they buy the restaurant ETF and they all go up and there's no discrimination between cheap and deer or they say we don't like restaurants

and they all go down likewise no discrimination so I think that this as an active manager what you really need is that dispersion and that dispersion has been tamped down, which really makes their job more difficult. And the third thing I'll say, which is what we've been spending a lot of time on recently, nothing's imminent on this, but is on liquidity. And, you know, just as a very simple picture, you know, when you think about liquidity, in liquidity is just this idea of taking cash and translating into an asset or an asset into a cash with minimal price effect and fairly cheaply. So you think about the simple scenario, there are liquidity demanders, there are liquidity suppliers, and they're market makers, because there's a temporal difference between when we might want demand and supply. And you say, let's write, you know, let's like write who those characters were 15 years ago.

And this, let's say, pre-decimalization, who were the suppliers, demanders, and market makers, and then look at it today. And I think that the picture is really quite radically different. Historically, by the way, liquidity suppliers were active managers. If you look at simply the mutual fund industry because of outflows and performance, all things being equal in last decade, there's $3 trillion less in active management than there would have otherwise been, had they just been able to keep up with the market and not lose AUM. The liquidity demanders, like you mentioned, the behavioral gap, I mean, there will be a day when people have piled in index funds, and especially the ETFs are going to freak out.

They're going to freak out. It's going to be some geopolitical thing. It's going to be some sort of economic dislocation. Or it could just be something endogenous. And they're going to want to sell. So our traditional source of demand have been hamstrung or liquidity providers. And then let's talk about market makers, because it used to be some person on the New York Stock Exchange trading in eighths who had a responsibility to keep an orderly market.

Now we see high frequency trading, right, which is predominantly the source of liquidity, which is absolutely fantastic in normal environments. What we really don't know is how those things are going to show up in difficult environments. And I was just reading Andrew Lowe's new book, which we could talk about it, which I would recommend. And Lowe has got this really, and he wrote a paper about this, but a really interesting analysis of August 2007, which is a quant, this quant meltdown.

And he basically said it turned out that a few of these HFT, high frequency of trading firms, lost money, which is very rare. And so the next day they just didn't show up. They just didn't show up. And so what happens if something anomalous, what happens to our market-making mechanism? So to me, these are the things that,

these are the distortions that I've been introduced in up markets and things are going, okay, you actually don't see really much evidence of this stuff or it doesn't get you worried. But if we get some sort of a drawdown or some sort of concerns, I think these things are surface and that'll be interesting. And it'll be an opportunity, of course. It'll be a challenge for a lot of people, but an opportunity for others. I was really interested by the section on names that get into the S&P 500 and how pretty much instantly their price to books tend to go up, their price to earnings tend to go up relative to similar size companies that are not S&P 500 constituents or, you know, normalized for some variables like that. And you see this

kind of everywhere that, that kind of whether or not a stock is in a certain set of indexes or ETFs may have more and more of a role of its return. Now, the problem is, again, that can go on a long time. So even if there's a massive distortion, you know, active managers need to keep the lights on and, you know, they could be, they could be insolvent before they're right. So that seems to be when I hear all the same frustrations you do from my colleagues, it's like this looks, everything looks so, there's so many things that look odd, you know, massive opportunities in certain places that just don't seem to be being rewarded. And tons of, you probably talk to more than I do, really smart hedge funds, long-only managers

that are just basically throwing up their arms and saying, this just isn't working like it used to, and I'm out of the business. So one other thing, Patrick, I want to come back to is this behavioral gap because you've mentioned that, and that's super interesting. and I guess two thoughts on that. The first thought is just to be sure we're all in the exact same page. So behavioral gap, this is work done by, originally I saw by Ilya Ditchef at Emory University, calculating the difference between basically buy and hold strategy,

time weighted returns for a fund and dollar weighted returns, which is essentially an IRR calculations that takes into consideration of flows. So people, if you buy high and sell low, that's going to be bad. And if you buy low and sell high, that's going to be good. And so Ditchef actually did a study around the world, about 19 different countries, and he found that on average that that behavioral gap was 120 basis points per annum. You think about that, that's like a gargantuan number. It's an enormous amount of money. Back to the raw dollar terms.

It's a lot of money, raw dollar terms, exactly. So I guess a couple points I would make. Number one is that it's interesting that through 2015 and the 2016 numbers are likely to be a little bit different. But the 2015 numbers, we saw that the average index fee. was 20 basis points across all the products, and the average active fee was 80 basis points. So you have your index return, the average index funds earning 20 basis points less. The average active managers earning 80 basis points less. So there's a 60 basis point differential between active and passive.

And that's what people dwell on. That makes a lot of sense. What we're saying here is we have to layer in another 120 basis points for the behavioral gap. So saying that differently, the behavioral gap is twice as important as the fee differential between an active pass. So while, of course, we should be fee sensitive, we should be thoughtful about all that stuff, by the same token is actually behaving well is going to save you a lot more money than that fee differential. So that's the first point to make. And the second thing is there is a way that this doesn't seem to make sense because investors are investors and in a closed system,

you know, someone's got to win, someone's got to lose. And here we are saying the investors are losing 120 base points per year. Like who's winning? Who's on the other side of that table? And there was a great paper on this, and this is actually kind of new to me by Richard Sloan. that was written, it published in 2015. It's called wealth transfers from equity transactions. And Sloan's point was, you know, it isn't a closed system. It's an open system. And there are other entities that interact with investors, the most prominent of which are corporations. And you already alluded to this. So companies are repurchasing stock. They're issuing stock. They're paying dividend. They're buying one another. They're overpaying for one another. Right. So they're interacting

with investors in a way that can generate different returns. So, So one example I'll give the most trivial example I can think of is, let's say we have a company that we agree somehow objectively is undervalued. Their assets are all just cash or something. So we know the stock is undervalued. Then that company repurchases shares. Well, if someone sells the shares, that is a wealth transfer from the seller who is getting less than what the stock is worth to the ongoing holders who are now getting a larger slice of the pie. So that's just math. And right? So there's an example where the company itself is instigating this wealth transfer that really isn't me taking it away from you or you taking away from me. So the Sloan paper is really interesting. And he documents these data going back, you know, it's like 20 or 30 years. And this is actually a much larger effect than I had fully anticipated. And like most of these effects, it tends to be more important in some environments, again, where companies are issuing or repurchasing more equity and less important in other environments. But you get idea. So super interesting. That's a nice piece of the puzzle that clicks in. And actually in the paper,

he says, oh, yeah, this helps reconcile the Ditchf work on dollar way to return. So it all sort of fits in it together nicely. So let's even go a level above that. So all these numbers, 20, 80 basis points, 120 basis points are all talking about the public market for the most part. But now we've got this really interesting phenomenon of this lower propensity of companies to list their shares either at all or just waiting a lot longer to do so. So, you know, the 120 basis points is a piece of an public equity return, but you had Amazon coming out at $600 million evaluation and its IPO. You know, Google, I can't remember. Facebook at $110 billion. So $110 billion of wealth is being created and that's not being captured by public markets investors.

So maybe you could riff for a moment on this interesting trend that seems to be specific. specific to the U.S., maybe you could explore why that's the case as well, of companies that are just less prone to list their shares at all, meaning a lot more of the values captured in private markets. So, Patrick, I have to say, for some reason, this topic absolutely fascinates me deeply. And I don't know, it's like I can't, I can't get enough of this basic issue. And just to replay what you just described is the number of public, publicly listed companies in the U.S. today is less than what it was in 1976.

You know, GDP, real GDP is 3x, population growth, something like 30 or 40 percent. And we were a much bigger economy with many more people, and yet we have fewer listed companies. And by the way, it's very beautifully symmetrical. It goes from 76 up to 96 at a peak and, you know, basically doubled. And then from 96 down 50 percent. So we have half as many listed companies today as we did in 1996. So you're exactly your point. It's like, what the heck is going on here?

and, you know, it's basically math. It's addition and subtraction. So what adds to the population are IPOs and spinoffs, and what subtracts from the population is essentially M&A, but there are other delistings and so forth. So if you step back and examine that, yeah, it turns out the story is actually pretty straightforward. It's actually about half of it comes from less listings

and half of it comes from delistings, but there are a few components to it. First is M&A. M&A has been pretty robust in last 20 years, but what's a new component, as you pointed out, is the private equity world has come along. So private equity buyouts in particular. So that's a new player on the scene has basically taken companies out of the public circulation, and they essentially haven't come back.

The exit, number one exit strategy for private equity guys is to sell to other companies. And then the second strategy is to sell to other private equity firm. So their exits are not going back to IPO. So they're taking them out, putting them back in. M&A itself has been robust, obviously, and that's led to actually a lot of consolidation and concentrations in industries. And then we've had a total dearth of IPOs, and we averaged from 1976 to 2000, something like 280 IPOs per annum, and we're down to since 2000, about 115, something like that. So it's been really slashed more than in half. So that's a really

interesting setup. But taking one big step back, to your point, I think this is something that's really important. So, you know, let's say you're the CIO of Notre Dame, your alma mater in 1976, and you want exposure to U.S. equities. Ain't that hard, really? All you do is you basically buy, you get early stage venture, perhaps, which is just getting going, and you bought the S&P 500. You're pretty much in good, good shape. Now you're the CIO and you want exposure to U.S. equities. To your point, you need early stage venture, late stage venture, private equity buyouts, and public markets. And guess, guess what? That's great if you're the CIO of Notre Dame, but the rest of us don't have access

to all those things or certainly not cost-effective access to those. So this is another thing is not only is it very different. It's like the regular people can't avail themselves of these opportunities, which is all really interesting. Last thing I'll say is you pointed this out, this IPO thing is really fascinating, which is, and there have been some slight, I think some slight regulatory changes that have allowed this to be more to facilitate this to some degree. But it used to be the case that, you know, when your company was, you had a young company, it seemed exciting, you would go public. And as you point out, the numbers you get, I'll just repeat them because they're just so mind-boggling that Amazon.com went public almost exactly 20 years ago. And in,

and current dollars, so 2016 dollars, the market gap was $625 million. It's now worth something like, I'm at a ballpark, 450 billion. So in a sense, if you bought that on the IPO and just sat tight, you obviously would have done very well, and collectively it's done very well for its shareholders. That's great. Facebook went public about five years ago. Facebook's an amazing company, too, but they went public, as you pointed out, with $110 billion market cap. So they've done, they're also worth something like $450 billion.

That's great. But there was $100 billion of profits that occurred before anybody could, before saw the light a day. And so the question is who's capturing those economic rents? And as you know, I mean, I don't know if the numbers today, something like 150 so-called unicorns, these are businesses, private companies with market capitalizations, at least based on a recent round, of a billion dollars or more. So if you add up the aggregate value of these unicorns, it's something like $5 or $600 billion that's sort of sitting on the side, whether that's real

money or not. If those companies went public, they would accrue that market capitalization. That's a robust debate in and of itself. But that's a, that's, you know, that's a, that's an Amazon.com or something bigger. The Google sitting on the side. with no access. So this is a, it really has made the scene more complex. And then it goes back to our thing on, you know, competition, which is some guy did a calculation, which I thought was pretty cool. I, you know, the time, I think there's something like 140,000 CFAs, something like that. Yeah, that's about right. So this guy did a calculation of, I guess you'd say CFA charter holder. Charter holders, yeah. I just violated my thing I signed every year. Okay, so no, we're going to

correct that. So this guy did a calculation of the CFA charter holders, by public stocks and has gone ballistic, right? So you think about like 1976, there weren't that many CFA charter holders, and now there are a lot more and the number of stocks come way down. So it's really interesting. So there's, yeah, a lot of smart people analyzing fewer things and it becomes difficult from a public market's point of view to try to distinguish yourself. It would be fascinating to know. So if you've got the behavior gap at 120, what the, I don't know, we'll call it private equity gap is, meaning what chunk of return, if you assume the long-term return to U.S. stocks is like it has been in the past. Let's say it's 7% real or something like that.

Like what chunk of that is being captured by this falling preponderance of companies listing? I mean, who knows how you would calculate that? But that could be a large number that people have to think about. And then the question is, okay, so who's capturing all that value? What happens to wealth inequality? You know, that's, you got Zuckerberg. I don't know what his number is, but it's a huge one of that 110 billion. So there's, There's just fascinating market dynamics happening underneath all this. Why, how could this change? So your friend Bill Gurley has a great quote.

I always love using, especially when talking about private markets, which is something like you haven't accomplished anything until you're liquid. And for the obvious reason. And he's a big proponent of more companies going public. I think literally just yesterday or maybe it was even this morning, Fred Wilson at Union Square Ventures wrote a piece about kind of an ode to going public and thinks that a lot more companies should go public because when you're public, it imposes transparency, accountability. It makes companies better that if you're going to lose your biggest customer or have a management

problem, it's very public. So it imposes sort of a discipline on companies, which is a good thing. So if there's these positives to being public, what are the negatives that are holding companies back? Why isn't an Uber public, for example, maybe not them in particular, but more generically? And is it something we can fix? Because it seems like we've got a number of smart people agreeing that public companies are listing publicly is good. That's probably better for like the shareholder base of the country via pensions, et cetera, so they can capture some of this upside.

How might this change? How might this kind of sine wave reverse again? Yeah, no, exactly. And by the way, we have a new, you know, we have new leadership in Washington that's, the head of the SEC basically said he wants to increase the number of publicly listed companies. So there's at least some initiatives that are going into that. Look, I think that the way to think about this is a basic cost-benefit analysis. And I think the companies have deemed the costs have gone up.

By the way, I mean, Sarbanes-Oxley, something people point to 2002. This trend was well in place before Sarbanes-Oxley. So it's clearly not just Sarbanes-Oxley, but that raises the bar to some degree. And we see that in things like the median age of an IPO is the companies are older than they used to be because they have to be a little bit bigger to be able to shoulder those costs and so forth. So the cost has gone up, but I think there's some, there's some costs that some companies complain about may or may not be real, but things like the cost, you know, quarterly reporting and competitive disclosure and so forth. And then there's the benefit side, right? And you pointed out the benefit is things like liquidity. I think that certainly post-World War II, there was a need for capital as we were opening up a huge market in the U.S. and there was a lot of physical capital required. The challenges may have companies today, you point out in a number of these names, they're really

quite capital light, so they don't really need that much cash to invest in their business. Another thing else on this, Taf said some of this is it turns out that there's these sort burgeoning small markets for liquidity, even for employees. You say you started some startup and you're getting all the stock, but the stock is not monetized. You can't do any with it. So for example, in the fall, Airbnb did a large round, like it was $800, $850 million, and something like $200 million of that money went to buy out employees. So in a sense, it's a backdoor liquidity for those and for certainly those employees to cash out. So part of it's just going back

to the fundamental case is can we get this cost benefit analysis tilted a little bit? And I already mentioned, you know, the fact that there is this avenue toward late stage venture. And by the way, if you're these big mutual fund companies and this is not eluded these guys, so you go to Wellington and Fidelity and T. Roe Price, they have been very large investors in late stage venture. And, you know, it's not exactly what they do, but they've been going headlong into it because they're trying to say, and I think there's a quote from the guy at Fidelity where he said, pre-IPO is the new IPO. So they're trying to get part of that action. But again, it's a little bit of the Wild West. And we actually put it in our piece. The Wall Street Journal does a nice job of documenting

all this. But it turns out that these investors are going in the same round, paying the same amount and then they mark them at different prices at the end of the quarter. So it was like there is no, even for those guys, there's not a ton of transparency. I think that I'm very sympathetic to the Bill Gurley point, you know, where he basically said that IRAs for venture capital firms are like sort of all-time highs because these unicorns are getting marked up and it's the last $100 million sets the price and liquidity is an all-time low. And he's like, look, something's got a balance out here or something somewhere, right? We have to be able to monetize some of these things. And, you know, I think he's got afraid, I don't want to put words in his mouth, but he's sort of saying,

we're stuffing a lot of capital until these very immature companies, and in part because we're racing to see other unicorns and so and so forth, and we want to get the next hot thing. And some of them may not have the maturity, really, to be able to deal with that. So, like, right, IPO processes, you need to have adults. It's audited financial statements. There's got to be processes and procedures in place. And there's a discipline that's get instilled as a consequence of that that probably is a good thing. Now the flip side is, and I don't know if Snap is an example of this, but the flip side is a lot of these companies are carrying or sporting valuations that if they're put up against the test of market forces may not hold up. So I think that's probably something that's

also in the back of people's minds is we can live the dream of our valuation without having to really test it specifically in the sort of spotlight of the capital markets. Speaking of the journal, they via CalPERS put out some fascinating data about gross and returns. And sure enough, the returns both gross in net to, let's say, private equity buyout funds have been really, really good. A number of people I've had on the podcast have talked about how private equity is in its heyday, that it's experience in a massive growth in AUM because of all these things we're talking about. But what I found amazing is in the world of 10 basis point vanguard ETFs, the total piece of the pie, that is, fees taken by the private equities from

CalPERS returns was almost 40%, meaning, yeah, they got an 11% net return, which was better than pretty much everything else in their portfolio. So great. But the GPs were keeping, you know, 7% or whatever the exact number was. So there's, I wonder if there is, I'm going to, we're going to circle back to the asset management industry later to use it as an example of how to evaluate a moat or lack thereof. But I find it interesting to think about a way in which mainstream investors might get access sort of like a vanguard of private equity or a vanguard of venture capital, which may be

oxymoronic, but I do think that we might see attempts to dig into those earlier parts of the market and do it at cheaper prices in the future. What do you think? Right. No, I agree with that. I think that there are some important things about private equity to bear in mind. I mean, first of all, to some degree, the private equity firms have some latitude over the timing of what they're doing, right? So they're going to buy, obviously, when they think it's somewhat advantageous, they're going to sell when they think it's somewhat advantageous. And so that ability to have at least a few years between that point of the buy and the sell, it may be to some advantage of them.

By the way, I have to mention there was an article. It was awesome. Like, I don't know, a month ago, there was an article in the Wall Street Journal about a European buyout fund that was creating a fund to buy companies from itself. I'm like, that is nice work if you can get it. And actually the premise, you know, which is probably not unreasonable, as they said, you know, the typical time horizon for a private equity firm is, let's call it, you know, so three to seven years.

And actually, we can build value for much longer than that. So if we buy it from ourselves, we can extend our time horizon beyond seven years, and it's really great for investors. So that's the first thing. And the second thing is, and this goes back to our discussion a bit on liquidity, is that it's important for investors always understand. And I learned this from Peter Bernstein many years ago, is that there tends to be a tradeoff between control and liquidity.

which is to say if you want lots of liquidity, you should realize you're not going to have a lot of control. And if you want a lot of control, you should realize you're not going to get a lot of liquidity. And so, you know, on the one extreme, you know, one extreme might be a private company where there is no liquidity, but you might be, you know, it might be your company, right? So you have all the control, no liquidity or very limited liquidity. And you want to think the complete opposite is public markets where you buy and sell a day trade or whatever it is, right? No control. lots of liquidity. Private equity is obviously somewhere between those two extremes. And so that's the thing to think about is, and it goes even back to our endowments, that endowments obviously are now

moving, I think, have been moving steadily into less liquid assets, recognition or at least believe that there's some sort of liquidity premium. But that requires that you don't demand liquidity, you know, when you snap your fingers. And that's this tradeoff that I think people have to be careful about. So the component for private equity is that control component. And, you know, they, are in control of these assets. And if something goes wrong, there's something they can typically do, whether it's changing management or changing strategies or whatever it is. So this is this sort of fundamental tension, right?

As you and I, as individual investors, really would love to have liquidity. We see those private equity guys, like you said, doing really, really well. We'd like to participate in that. And yet, if we do that, we're going to give up something, which is typically liquidity. Maybe this feeds back into the whole, you know, propensity to list. but you find interesting mismatches even in that paradigm of control and liquidity where Snap, for example, he brought up Snap before. So there's liquidity, but now complete control, like even more complete control by Evan Spiegel,

who basically doesn't matter how much he owns of the company, the public was perfectly fine buying shares and saying, we're never going to be able to vote you out of here. And you see more and more of these like dual share classes where the founders or the CEOs have controlled despite a minority interest in the company. And wow, is this like an interesting dynamic for how some of these things might play out? Yeah, and I think that these dual class, there are some instances where there could be some justification,

certainly in media, you know, for example, just to make sure there's some objectivity in what they did. But, yeah, these more recent examples, I mean, I think Google probably opened up that door to some degree and Snap came, you know, flying through it. You'd like to have the shareholders at least have some rights to be able to some rights in case something goes wrong. So, yeah, you know, people are there, it's an open market.

So if you don't want to participate, you don't participate. But I would just say that in terms of hygiene for governance, you know, those are, that's not, that's not best practices. So we've talked almost exclusively about very metal level stuff and frameworks, but most of us got interested in this because we liked looking at businesses and buying and selling securities. And there's still a number of people out there that want to productively analyze securities. And two of your, you know, six or so recent papers really caught my attention.

The first one is the base rate book. And the second one is an updated really seemed brand new to me, paper on MOTS. And so I thought we'd spend some time on the kind of key ideas in those two papers because I found the combination of those two to be really an amazing toolkit, if you will, for how people, analysts might think about evaluating a company and industry and investment opportunity and so forth. So we'll start with the base rate book. Maybe you could begin by explaining just exactly what a base rate is.

And then we'll talk about kind of the framework, the combination of the inside view, outside view that you develop in that paper for how to effectively make predictions or forecasts about the future, which we know are difficult. Yeah, Patrick, I mean, I'm I also am super excited about this. And sometimes I reflect back on, you know, what do I wish I knew was a younger, younger me? Or what would I tell my younger me? and I think that some of these ideas around the base rates would be some of the first things I would say. The other thing I would just tell you that, like you said, we're flying at 30,000 feet, but I still have this, I don't know, romantic view of myself as an analyst.

I was an analyst for many years and I still think of myself at an analyst, even though I'm not really sure. I do much of that anymore. But I think the inside, outside view, the base rate stuff is really a very, very powerful set of mental model. So, yeah, to your point, let's delve into what that is exactly. So I think the idea of the inside outside view is most widely associated with Danny Kahnman, the basic ideas as follows. If I present a problem to you, and it could be anything, it could be how long will it take it? I'm sure it'll cost to remodel your kitchen. It could be when will you hand your term paper and if you're a college student, but more importantly, how is this stock going to do?

The classic way to analyze it is to gather lots of information, combine it with your own experience and input, and then project into the future. So if I say to you, hey, how is X, how is X, Y, Z company going to do? you're going to read a lot about it. You maybe read some other people's views, and then you're going to build a model. And that model is going to reflect all those things you've gathered. It's going to reflect your own experience and your own biases, and then you're going to go and project into the future.

The outside view, which is equivalent to a base rate, so the outside view would say, what happened when other people were in this situation before? So it's a very unnatural way to think, by the way, for two very specific reasons. first is you have to leave aside all this cherish information you've gathered you've worked hard on this you've built this model you've been very thoughtful in it and so on and so forth you know you're going to be loath to set that aside and the second is you have to find an appeal to the base rate you may not know

what the base rate is so those are two reasons why we don't use it and so so conman other psychologists have have pointed out that the thoughtful integration of the inside view your own work and the outside view the base rates leads to better forecasts And by the way, I want to come back to one of the key ideas here, really, is something that everyone talks about in the investment business that almost no one quantifies effectively, and that's regression toward the mean. So regression is a very subtle and tricky concept. I think most people think they understand it.

Most people don't. And if you understand this inside, outside view, it actually give you among the best sets of tools for thinking about not only the fact that regression happens, but the rate at which it happens. that's super important. So when I was on the by side, you know, we were doing all this modeling and so forth. And before I tell you, I should probably give you one example of the inside, outside view. So about, you know, March or something like that, there is a cover story of The Economist on Amazon.com. And Amazon's an awesome company, and I'm a shareholder, I should tell you, and I use a product, I love it's great, so forth. So in this article, it says that an analyst at a particular brokerage

firm says, we think that they will grow their revenues 15% through the year 2025. Now, to give you some context, Amazon did their sales in 2015 were 107 billion. And 2016 were 136 billion. So 15%. So we backed up. So we'll start with a 2015 base, 107 billion. And we're going to say 15% for 10 years to 2025. So they're already ahead of path.

They're ahead of plan, right? So the first thing I would say is that how did that analyst come up with that 15%? 15% growth. And I suspect if you looked at his model, and I'm sure he's got a great model, it would be incredibly compelling. He'd say, oh, US, you know, US retail's this big and e-commerce is this and Amazon shares that and they're going to do, you know, and he could walk you through that and you'd be very convinced. He's, oh, international, it's even bigger and here's what's going on e-commerce, so-and-so-and-and-so-for, just getting going, very compelling. AWS, dominant cloud business, so-and-so-and-forth. You go through those steps and you'd be like,

I'm on that, I believe it. That's the inside view. The outside view would say, well, if you examine every company adjusted for inflation since 1950, top thousand companies of the world, that started with $100 billion of revenues and asked how many of them grew 15% or more for 10 years. Now, it turns out because that's such a small group, right, there are only 313 companies in that sample. It's not nothing, but 300. Turns out exactly zero of them have grown 15% or more. By the way, the average growth rate, almost all the companies are between. negative five and positive five. I mean, they're basically GDP-ish. So now the question is,

what probability would you place on the probability of Amazon reaching 15% growth? And, you know, it's not zero, but it's not 50, I would guess, right? So that's a good example of how that works. So I would just say when I was on the buy side, I was always, I thought these, I knew these ideas and they're very powerful. I just never had access to the data. So when I rejoined Credit Suisse, we stepped back and said, all right, I put on my whiteboard, like base rate book. This is sort of I aspire to do this. And we haven't had incredible resources with our whole database where these guys have incredible data, including dead companies back to 1950. It's all adjusted for inflation. Really nice work. So we have been methodically going through measures of corporate performance,

including sales growth, which is where the Amazon numbers come from. We did gross profitability, leveraging work by Robert Novi-Marx and others. We did operating leverage, which drives me crazy. because operating leverage is what usually analysts screw up. So how do you understand that? We did operating margins and we did return on capital patterns and earnings growth. I think I got six. And so we documented basically the sweep of corporate history. What's also interesting about each of those measures is that allows us,

we can do these correlation work that allows us to understand the rate of regression toward the mean. So some of these things, for example, earnings growth rates have very strong regression. So no matter what your forecast is, you should be aware of the base rate and regress your number toward the mean a lot. Other things actually don't regress very much at all. It turns out operating margins have very low regression, interestingly. So it's all quantitative, and you can look at this. And so to me, it's just this incredible contribution to how you think about modeling a business

and to your point. And I'm constraining this discussion to, you know, basically income statement and some balance sheet return on numbers. but you can now open up your mind on this and you can see these things everywhere, right? So M&A, how do I think about M&A? Well, there are great base rates. And we know the sweep of all M&A deals, but then you can start to say, well, what if it's a small premium versus large premium, a cash deal versus a stock deal and the near-in acquisition

versus a different industry acquisition? You start to cut the data in different ways, incredibly informative. And again, most people just don't do it. They blow it off, which is really interesting. Professor Demoder in NYU has a new book called Narrative and Numbers. And I kind of think about narrative as being the inside view and numbers as being the outside view. And what I think is interesting here, and we've talked a bit about this offline, is this emerging idea of man plus machine, which I almost think about as the same thing as inside plus outside. So the inside being sort of domain expertise, the model that the analyst built on Amazon is the example, and the outside view being sort of whatever you want to call.

all it, big data, analytics, basically building base rates, right, of what to expect from a class, not from just an individual example. So let's talk about how you could actually plug those two things together. There's literally a simple formula to kind of tell you the general idea. Maybe you could describe that formula, and we could get into this idea of what a shrinkage factor is so that people that are interested in this idea, especially analysts, might start to combine. It's not just one or the other, I think that what we're finding in all sorts of fields, be it free chess or any sort of analytics, is that the combination of inside and outside might be better than either or. I love this nerding out. This is awesome. So the formula is, obviously, you know, it's called

true score theory, but the formula would be something like making a forecast. So basically your forecast would equal the average of what you're looking at. And we should probably make, let's see if we can get a concrete example, batting average for baseball players. So you can look at the batting average of all the baseball players plus a shrinkage factor. So little C, and we'll leave that aside for just a second. And little C is multiplied times your recent batting average minus the average of all the players. So you might be way above average, way below average, right? It's the difference of your performance relative to everybody else.

So the shrinkage factor is sort of the key idea here, and that takes a value between zero and one. So a shrinkage factor of zero means that even if you've got great performance or horrible performance, I basically throw it out the window. And my best estimate of your next outcome is just the measure of the average. So low shrinkage factors means it's a lot of luck and your exceptional performance, good or bad, basically throw out the window. And you might think about things like rolling dice or lotteries and those kinds of things. If the shrinkage factor is won, that means I preserve your extraordinary performance.

and we expect that to persist, to perpetuate. So what we can do is using some basic simple statistics, these simple correlations, is we can start to actually plot the shrinkage factors for various things. And so just to give you some sense on this, I'll give you sort of three little data on this. One is the shrinkage factor for returns on capital for consumer staple companies. So, you know, you're looking at Procter & Gamble and Unilever and Kellogg's and all these

great businesses. their returns are pretty good, what will they be next year? And the answer is, roughly speaking, it's a shrinkage factor of 0.9. It's nine parts what it is today, one part the average, and you're in business. So they're going to be pretty persistently good. On the other extreme would be, let me predict the S&P 500's performance. So I'm going to do that by looking at on my X axis T0, last year's result, how the market do it?

And then how does it do in the subsequent year? Year-over-year performance. If you plot that correlation, and you can do this for MECI World as well, back to 1926, the answer is zero correlation. There is zero information from year to year in stock market performance. So that says your most parsimony's estimate is some measure of the average. And as you point out, I mean, I think you should give the number 7% real. That's probably not a crazy number.

Maybe, you know, six to seven real probably is what I would say. And maybe it's an uch lower than that today because of interest rates, but basically that's the ballpark. And there is no better estimate. Now, if you go out three to five year numbers and you roll those out, you get a little bit more signal, but for the most part, one year numbers basically noise. So I just want to make sure I want to use the baseball example again just to make sure that I'm thinking about this the right way and then we'll move to a company. So let's say we've got, you know, modern day Ted Williams or something batting, batting 400. The league average is 250 or something like that.

So he's got a 150 point premium. The idea would be that if you're, if you've got that historical view or maybe you even have an estimate for that 400, that you think it's going to be, that if you're trying to say, okay, what's he going to do next year? We basically look historically at how has Ted Williams kind of performance related to his future performance? How persistent has it been? If it's completely persistent, you get a one, meaning you can expect he's going to do basically the same again next year. If he's had some variability and it's point five, your forecast would still be that he's going to be better than 250, but it's going to revert towards the mean. So shrinkage is basically a measure of how quickly does something revert

towards the mean. Precisely. And, you know, it wouldn't be just Williams versus himself. It'd be all baseball players versus themselves. So you can do this. You can get much larger sample sizes and you can examine the rate of regression toward the mean. That's exactly right. So, so the first thing that everyone should be, you know, just to state the obvious to some degree, if any time the correlation between a measurement A and measurement B is less than 1.0, you have regression toward the mean. That's just math. Okay. So there's so and by the way, in almost everything in light, that's going to be the case. So this, the shrinkage factor, tells you the rate at which it happens. That's exactly right. That's the key insight. It's not that it

happens because almost everybody knows it does happen. The question is at what rate does it happen? And again, different, you know, even things like return on capital, I mentioned the consumer staples companies, but it obviously is different regression rates for different industries, which makes intuitive sense. So the consumer staple guys are going to regress a lot slower, lesser rate, than say energy or technology, where there's more competition and so forth. So understanding those differences, and then this cycles back to your model. So again, you're doing your bottom up thing. You want to make sure that you're informing yourself, understanding these basic economic

drivers that have been around for a long time. And again, we're doing these things with very large sample sets, you know, very long. So these are pretty robust. And going back to our Amazon example, could they grow 15% or more? Of course, it doesn't mean that they're not going to do that. But it is a mechanism to temper your views in a way that it gets you in a more correct place. I remember probably like every active manager I had to think deeply about the fang stocks a couple years ago because if you didn't know them, you were in trouble. And basically I did the same thing, which is to say, let's assume that at some point, these companies have to trade at a reasonable, they have to trade at some price to earnings multiple that's in this stratosphere.

And we'll give them a long time. Let's say it's 10 years before they do that. Like Apple eventually came down to Earth. And then let's say, okay, what do they have to grow their earnings by to reach a 35 P.E? or something, assuming their microprice grows kind of with the overall markets. And then you go look at the base rate and it was like 0.2% of companies ever, even including microcaps and small caps, had grown earnings at the rate they would need to to justify even a high PE 10 years from now.

It makes me wonder, I always, you know, I hear your frameworks and I read your papers and I always think, okay, what is in here that I can pour into some advantage in markets as an active manager? And it makes me wonder specifically with the consumer staples example. When you look at consumer staples returns on equity, even their stock price performance, you get these incredibly both high and steady charts historically. And it kind of doesn't make sense when you look at it. It's like, why are these simple companies, there doesn't seem to be any mean reversion. It just they're constantly earning these nice ROEs delivering really great returns to their investors.

And then you compare it to an energy stock or an information technology stock where there's all this crazy kind of mean reversion and cyclicality and dispersion. etc. So where do you think as an analyst the most kind of applicable opportunity is here? How would someone think about, okay, I buy these steps, which is build a base rate, which maybe most people don't do and maybe there's an edge just in doing that. So build a base rate, which is kind of your baseline expectation, build a model for the inside view, and then calculate your shrinkage factor historically. Do you think that that points you towards specific sectors or industries or it could even be, you know, different equity markets like the Asian market. Like how do you think about the framework and then intersecting with current opportunity?

Yeah. So, I mean, it's a phenomenally important question. I actually think you, you touched on it really nicely with your example on the Fang stocks. So one of the key ideas, always to go back to in investing, is that you need to dwell fundamentally on the difference between expectations, what's priced in and what's going to happen in terms of corporate performance. and it's really the gap between those expectations and that fundamental performance that's the key to making money. So stock prices or any asset price reflects a set of expectations by future financial performance. So step one is really to say what has to happen for this stock price to make sense. Step two is to say, based on your strategic and financial analysis, how is this company going to perform?

And you're looking for mismatches. Those are your opportunities. So the base rate work obviously is going to be very helpful in guiding, that thought process, as you point out, if expectations are very high. By the way, sometimes expectations are high and they're fully justified. Company actually does everything it hoped and delivers results. Other times they're completely not justified. So that's, that to me would be sort of the key idea there to, and, you know, and they're also probably to state the obvious as you look around the world, there are varying degrees of market efficiency, right? So the U.S.,

and we talked about the U.S. number of public companies coming down by 50 percent. I think you made you alluded to this, but if you look at 13 developed countries that are comparable way of the data, their population of companies up 50%. If you look at all countries that are in the data set, they're up 30%. So here's the U.S., which happens to be 50% of global equity market cap, but it's incredibly competitive with fewer things to do. And you step outside the United States, right, and you get many more companies and, you know, less information and so on and so forth. So there's a continuum of efficiency. So some of these things can still be very informative and they're likely to be more mispricings in other environments. And even on this active passive, I mean, I try to finish the piece

we wrote with a little bit of a more optimistic point to say that there are still games that you can win as an investor. There are cases where you're competing against individuals, which tends to be more true outside the U.S. than it is inside the U.S. You may be competing with people who are doing things for non-fundamental reasons, which often can be true in the U.S. and outside the U.S. So there are ways to win. You just have to be very thoughtful about saying, why do I think there's an expectations gap, why do I think I can take advantage of it? Why do I have the upper hand here in this poker game? What would be an example of people buying or selling for non-fundamental reasons or a couple examples? I mean, just the classic example would be spin-offs.

And that literature has been around for two or three decades at a very minimum. But, you know, you're running a $100 billion funded fidelity. You own some stock and they spin off some little business and usually it's got a little hair on it and it's not that big and it doesn't fit your mandate. What are you going to do? You're using to sell it. I mean, because you don't really, it's not going to really affect your life one way or another. It's not going to really affect your portfolio performance. And so there's no fundamental thinking whatsoever. So that would be one. You know, another one to get a little bit more extreme would be things like John Geneacopoulos's work at Yale on the leverage cycle. So we, you know, this is a movie that's played out many

times. An asset price is going up. I mean, housing is the most recent and obviously tremendous large large example. Asset prices going up, people tend to lever that. What's important is not just that the interest rate, but also that margin requirements become very lax because everyone thinks they're doing great. And then the asset price rolls over. And not only, of course, now you're getting a margin call, but more importantly, the margin requirements become more stringent. And that accelerates the forced selling, and then you get the downside. So there's an example that you may get a margin call and you're selling not because you want to, but because you have to to meet a margin call, and that, again, should be presenting an opportunity for somebody on the other side.

And I actually think that in the financial crisis, we actually saw a lot of examples of that, that there were people that stepped up, some of the distressed guys that made a lot of money because they were there to, they were the liquidity prior that they offered the bid. And the seller was not happy about having to sell the asset at a certain price, but they had no real choice. One of the, to bring it back to perversions created or distortions created by ETFs, indexes, et cetera, one of the things I've been interested by is analyzing the set of rules that govern trading activity at, say, Vanguard or the crisp indices, really, that underlie Vanguard's industries or funds, rather. And a couple examples like, normally you would roll M&A or

corporate activity into quarterly rebalances, but there are certain thresholds past which they become a forced buyer or seller. So you mentioned share buyback. and issuance before, there's actually a 5% threshold past which they need to participate one way or the other without any sort of fundamental work. So you have all of a sudden like forced transactions. And this may be a little bit like picking up pennies in front of a steamroller, but it strikes me that you could, with a good understanding of kind of structural forced, non-fundamental buying and selling, become a distressed or opportunistic advantaged investor around the edges. I mean, I love that. And I think that to your point, you have to do a lot of

really thoughtful work on what those rules are and how to exploit them and so forth. Usually you have to be able to be nimble trading and cost-effective trading and so forth. But I love all that kind of stuff. Yeah. So that makes enormous sense to me. So not all investment organizations would be set up to take advantage of those kinds of things. By the way, even simple things like rebalancing. I mean, there is a lot of work on this, but rebalancing for these index funds, it costs

money to do that. And, you know, I talked to the Vanguard guys and they would say that their rebalancing costs are relatively modest, probably five to ten basis points. And they can offset some of that through other means. But it's an interesting, you know, there are frictions out there and there are opportunities for people that they have to do stuff based on rules that allow for opportunities on the other side. So we've got pricing pressure. We've got fewer public stocks. We've got more competition than ever. The CFA to public company ratios at an all-time high. This is becoming like an ad to not be an active analyst. So with that, we'll move into the last section, which is on MOTS. And this is

just an absolute passion of mine of, mode is the term popularized by Buffett. But really, the underlying idea is how can you and how long can you create sustainable value at? Sort of the difference between Munger always says, Berkshire is nothing more special than being able to get funds at 3% and invest them at 13 and have that spread be consistent through time, sort of, sustainable value creation. And I close here because one, I think it's just really interesting to look at the data historically for kind of what, what are the components of a moat, what might it look like, do they really even exist? How long can they last? And I think it's relevant not only for investors looking at companies, but also for business owners. So if you're thinking about how to

build and run a competitive business, you want to think in these same terms. Because arguably the most value in the entire economy is captured by entrepreneurs building businesses. So I'd like to dive through kind of the components of moat. Before I, before we do that, I just want to, I really want to emphasize how much I think you should go read this paper because I read the original one and this one's labeled update, but it's filled with a lot of really new interesting information. And it's really one of my favorite things that I've read in a while. So maybe you could start by outlining kind of your take on what a moat is.

And then we can dive into, however you won't choose to describe them, the components of what make a moat. So it's, you know, a lot of this, I think, is intimately tied back to the notion of competitive advantage. As a young man, I read the original Michael Porter books. I think very few people actually have read the original Michael Porter books in part because they're not that easy to read. I did do that as a young man. And interestingly, by the way, Porter very, I don't know if he ever has defined competitive advantage simply. I don't know if there's a line at a porterbook that says competitive advantage is, you know, fill in the blank. Others have done that.

So I'm going to say first that a competitive advantage exists when two conditions are met or two criteria are met. The first is you're earning above your cost of capital. So we're going to call that an absolute hurdle. And the second is you're earning a return in excess of your peers or your competitors. So if you meet those two criteria, you're going to say competitive value. So in a sense, what we're talking about with the mode, I think is a slightly more subtle concept, which is sustainable value creation. So how long do you earn above your cost of capital?

It's basically the idea. Now, let's be super, we can make this, we can go back to some fundamental, tying this back fundamentally to an valuation before we go on about how we measure the moat. And that is going back to the very famous, probably the most seminal paper on valuation, which is Miller-Medigliani, 61.

And they've got a lot of cool stuff in that paper. One of the things they say is, the way to think about it, you can think about the value of a business in two pieces. One is the steady-state value. So if they just earn their current earnings, forever, what would it be worth? And the way I like to think about that is, you know, like a restaurant chain. So, you know, some restaurant chain has a current number of restaurants that are out there.

And if they just kept going forever and earning what they're earning, what would the restaurant be worth? The second piece is now called present value growth opportunities. But basically it's the future value creation. So it says, oh, we're going to make investments in the future that will create value and markets willing to pay something for that. And by the way, we try to do some. And so that is the future restaurants that will be built that will be good. So we, by the way, do some back of the envelope calculations using a lot of work by Aswatamotam, by the way. And we find that in the history of the SPP since 1961, that that ratio is about two-thirds, steady state, and one-third future value creation. So it's just to be super clear that on average investors pay for every

dollar they invest. A third of it is for this future value creation. So this is not like a trivial thing. This is something we really need to figure out. So how do you, how do you? So how do you, you quantify that, right? Obviously, we can just observe the return on capital, so that's the first step, but how do we know how sticky it's going to be, is it going to go up, is going to go down, and so forth. So the way, we try to do this, by the way, a little background on this, that over the years, I've written a number of reports, usually, you know, we just, we do a lot of work on it, and the numbers themselves sort of talk, and we just sort of write it up. There have been a couple of reports that have been very difficult for me to write, and one of the most difficult ones ever was measuring the

moat. So it was actually, I hope it comes across as quite linear, and, you know, and, you straightforward way to do it. But it actually was for me an incredible challenge to get that together and to get it to make sense and to use it. So that probably speaks more to my limitations than anything else, but that's an important thing to say. So we like to do it in three parts. The first is what I call getting the lay of the land. Second is specifically looking at the industry. And the third is looking at the firm's specific advantage. And what I would say to an analyst is if you've worked on a company and you can't, A, understand, you can't explain where the return on capital is, whether it's going to be sustainably high or low, and what the source of advantage is, right?

If you can't explain those things, you don't go back, go back, right? You don't understand the business. So in terms of lay the land to take it at the top, just a couple, and I'll just check these off really quickly. One is we love to do these things called industry maps, which is a visual way to understand both the size of competitors, the interactions of the competitive set. and basically what I'd like to say is that anything will affect the profitability of your company should be on the map.

And just putting it together is a great exercise. And by the way, portfolio managers love them because it's a very synoptic view of what's going on. Second thing we like to do is these profit pool analysis. So profit pool, you can imagine on the X axis is some measure of the size. It usually invested capital. So how much capital is invested? What percent in different companies? So each company's broken up.

And then the Y axis is return out capital cost. of capital spread. So if you're above the line, you're earning above the cost of capital, if you're below the line, you're earning below the cost of capital. What's beautiful about that little construction is the area of that rectangle is economic profit by definition. It's R.C. So that's really cool. So you're getting a visual of that. And we do those things over time. So we'll look at an industry and we'll look at it in, you know, say every three years, we'll look at it and then we'll see how has the economic profit, have the pools changed over time. company's getting better or worse, is the aggregate, Benny, worse. And that gives you a really

nice, going back to the Azwas concept of narratives, you can create a narrative to say. And so one of recent examples, I think we put in the piece, was on airlines. And airlines is a great example. So you say, and I have no point of view on airlines, but clearly a very challenge industry over time. And in the last few years, we're generating very positive, strong economic profits. So the question is, has something changed? Is it something that's just cyclical? What's going on here? So you need probes a bunch of questions that are important. We also do this thing called the pricing test, which is, can the industry raise prices above the rate of inflation, about the rate of inflation or below the rate of inflation? And then finally, we do something called market share test.

I learned this, by the way, is great. I learned this from Bruce Greenwald, my colleague at Columbia Business School, and he'd been talking about it for years. And so one day, I'm like, I'm going to go search to see. And it turns out there's this paper written in the 1920s where this guy wrote about it. He's like, everything, nothing new under the sun. But the basic idea is pretty straightforward, which is you look at how much market shares is basically the average absolute market share change in a sector over some time. And the basic. And the basic. Basic premise is pretty straightforward is if the average market share change is relatively low, it's pretty stable business, right?

Industry's not moving around that much. If it's whipping around, like the market shares are going up and down a lot. Energy or something. Energy is an example. But, you know, even there are some really profound examples like smartphones. So, by the way, I had the CFO of Nokia. It was a really good guy coming to my class at Columbia Business School. This is probably 2009.

And I don't think people realize this, but Nokia in 2007 had, more than 50% of the smartphone market, a majority. By that time, he joined the class, 2009, they still had something in the 30s, market shares in the 30s. Now they're effectively zero. So how do you go in 10 years from a majority in the market to zero? Really interesting. So, and by the way, smartphones are interesting just even Apple,

which is a relatively small market share, but incredible percent of the economic profits. Okay, so that's the first section is lay the land. Then we get into the industry stuff. And I'll go through this very quickly, but industry, we usually do do classic Michael Porter, Five Forces. What I always say is don't do the Wikipedia version of Five Forces, right? Because that's what most people do.

But Porter himself wrote a really nice paper in 2008. It's actually quite dated. But it was a prize of the Five Forces. And it's just a really nice paper. He brings it up to date and more contemporary examples, but tells you, like, in his experience, how do people screw it up. And so that's really good. And then we also do value chain analysis.

And value chain is really important, is just to slow yourself down. and compare exactly what a company is doing versus other companies. And then we do the Christensen work on disruptive innovation. Now here's one of the key things that I emphasize is that at the end of the day, strategy is ultimately about tradeoffs. So if you're talking to a company or examining a company, the question you need to answer it is, what tradeoffs is this company making for its competitive position?

And until you get to the tradeoff answer, you really haven't figured out the strategy. And by the way, most executives themselves, you say, what is your strategy or what is your competitive advantage? They'll rattle on about or reducing our SKUs and our marketing programs grade. But they're really not talking about strategy. They're talking about operational effectiveness. To get to strategies really got to be about tradeoffs. And then the final step is, you know, the specific source of competitive advantage.

And I'll just use the generic Michael Porter's. You could say, roughly speaking, there's differentiation, which tends to manifest in high margins. relatively low capital velocity for your high returns on capital. And then there's the low cost producers, which tends to manifest in lower margins, but higher capital velocity. So there's some generic ways to think about that. So here, and by the way, I should mention in the piece,

and we can post it somehow or whatever. But at the end, we have a checklist. And I always like to say as an analyst, even if not every single thing on the checklist applies to the company you're looking at, you should check everything on the checklist. And if you've done that and you've done it, you know, faithfully, you're going to have a pretty good sense of what's going on. And I will just tell you, especially for your young analyst,

the first couple of times you do it, it's going to take you some time. I just warn you, it's going to take you some time. But you're going to get better at it. And as you get more experience, you'll go down the curve a lot faster. So to me, this is great. And the other thing I'll just say that it's interesting as an analyst is that data, obviously data are coming in all the time. And one of the challenges that analysts don't have any context to place that data.

And one of the really powerful things about something like measuring the mode is if you really done that work and you really understand that work, when data come in, you have a context to place it. And that really is the big payoff. So that's why you should labor doing this work is because everything then will have much better context rather than you're dealing with all these, what appear to be one-offs. So I think this stuff is really, I agree with that. I think it stuff's incredibly interesting. And there's more to do, by the way. And I think ultimately the holy grails for us to do, to come up with some sort of quantitative score. for the moats besides just the numbers that are the outputs and tying that back to valuation,

right? And I think the markets get these things to some degree. We've been talking about consumer stables. You know, they have higher multiples on average. The market knows that they're better businesses and we know that energy tend to get low multiples. Why? Because they're very challenged to sustain high returns. Market gets that, certainly generally speaking. But can we do a better job, you know, more systematically about linking these two concepts of competitive advantage and valuation? If you want just a super simple introduction to that idea and And it's two factors, so it's nothing crazy. But Greenblatt's formula, you know, the magic formula, is like the roughest approximation of this idea.

So it's return on invested capital, which is from the back to the base rate work. It's something that certainly cash flow return on invested capital has a summer version to the mean, but has some stability. So high current returns on capital tend to persist somewhat. And you could call that like a super simple definition of a moat. And then EBIT to EB, or EBIT to EV, like a simple value measure. and you basically do a two-factor model and say, give me the best moat for the best price, give me that combination, and buy, you know, 100 of them or something like that. But a much more nuanced calculation quantitative, something, it's our grill, too,

a much more nuanced multi-factor approach could be a fascinating way to build a model. Love that. And, you know, you could, there are, it's not super complicated. No, exactly. I think the green about things great. And I think that there are, there are some ways to get even a little bit more sophistication, right? So in other words, like you said, persistence for REOIC or REO, is going to be higher in some sectors and others. Just layering that in is going to help you. So if you get consumer staple at low multiple, that's likely to be better than, you know, an energy company at low multiple, something like that. And yeah, you can, you could tweak that a little bit. Yeah, you can just tweak it just a tiny bit and probably pick up a little bit more,

pick it a little bit more signal. But the basic idea is right, good business, low prices, right? That's what it's about. And, you know, the question is why they, why they for sale and so forth. But nine, that all makes sense. Could you give one example of the tradeoff? That's maybe the one thing I didn't follow totally clean. earlier of a trade-off for competitive position that from the standpoint of a CEO or something like. Yeah, absolutely. So there was a really nice book that talked about a great case study of a bank. And now banks, this is an olden day, so not the contemporary times, but banks used to be institutions that really had quite limited hours. So they'd be open from nine to five and you had to do a lot of your banking in person and so forth. And now they were competitive on, you know,

CD rates and so on and so forth, but basically they were limited on their hours. And there was a bank that came along and said, you know, a lot of our clients tend to be families and, you know, often two working parents. And, you know, the fact we're only open from five, nine to five is very inconvenient. Gee, what would happen if we opened much longer hours and we opened on Saturdays and times we're more convenient for these families? Maybe we could get more people to work with us. So it turns out that they could do that, but it was quite expensive because you have to keep the store open, have employees working, so forth. So they said their tradeoff is we're going to offer much lower rates on our CDs and on our checking accounts and so forth. So it's going to be a much

less attractive return profile for our customers. In return, we're going to get much more convenience. And so they went into business and that was their basic model and it was incredibly successful. So the tradeoff was greater convenience, lesser rate of return on the CDs, for example, versus less convenience and higher returns. So that's an example. So that's an example. of a conscious tradeoff, you couldn't do more hours and same rates because you lose money, right? So that was as simple as that. So that's it. And so it is interesting to think about how companies position themselves one versus the other. And there's a lot of work and strategy about, you know, sort of niches that you and I are competing with one another and you'll take one

segment of market. And I'll take another. And we don't, we're going to really communicate about it, but we sort of understand one another that we're going to. And so in those cases, we're actually doing sort of natural tradeoffs. And we see a ton of that, by the way, in nature. It's a very natural thing. In ecosystems, there's a lot of niche construction and niches so that people can survive. Yeah. So I've got an interesting kind of closing category, which is on applying all of this to the

asset management industry. So I'm 32. I've got at least three decades left in me, hopefully a lot more thinking about these things and trying to be competitive. And so I'd be curious to hear your kind of general take, given how many aspects of the industry you're intimately tied to. And one of the things that fascinated me about the paper on moats is that when you break down companies that go on to continue to do well versus those that do poorly, those that do well, a huge chunk of that is the industry itself that they're in.

A smaller portion, or a significant, but smaller portion is the firm-specific effects. Whereas in companies that are industries that continue to go on to do poorly, industry matters still, but company-specific or segment-specific matters is the most important variable. So with that kind of idea in mind that industry matters, but also companies matter, how do you think about the competitive pressures, the state that we're at as an asset management industries and where, if any, the opportunities might be for people interested? Yeah. And I, you know, I'll apologize a little bit because I tend to have more of a U.S. centric point of view than I probably should. So that's going to be a little bit of a bias that comes through all this. The first, just in terms of U.S. and especially U.S. equity markets, you know, I think we talked about this a few moments ago that I do, and you mentioned a good example of stocks going in out of the S&P 500. I did express some of these concerns I have about valuation and intersector correlations and liquidity. My own sense is there will be there will be some risk, there will be some point. There will be a liquidity provider at some point. It may not be in the next quarter or the next year, but at some point being prepared for that, there will be opportunities. And one of the ways to do that is to say, I'm going to look at different businesses or even, bonds or whatever it is and say, at what price do I think this is a really good value, right?

And so just sort of preparing yourself almost like a pre-commitment at these levels, this is something I'm going to participate in. So there are some opportunities. And by the way, money managers can do that as well as individuals. The second is I do think that we've seen a large move into alternatives. And I think that probably continues to some degree. You mentioned before about, you know, the CalPERS experience of how much of the economic rents go to the manager and how much the economic rents go to the client. I think that's an ongoing discussion. alternatives tend to be a less efficient game. And there are some things that certain asset managers can do

or ways, sets of expertise that they have that make it somewhat more unique and a better fit for what they're doing. So again, you're looking for the easier game. You're the smartest player at the table. That, I think, still does to make some sense. The third thing I would just say is that, you know, which we talked about a few moments ago,

is that it's a big world out there. And notwithstanding the U.S. is a huge chunk of the global equity market. And if you take U.S., Japan and parts of Europe, but it's a big slug, it's a big world out there. And there tends to be less efficiency in other parts of the world. And so that would be another threat I would put out there is say, might there be other parts of the world that are less efficiently priced? They tend to not be as big.

So the scalability issue is potential, but they will grow over time. So that would be the last thing I would say. So often my students will come to me and they'll say, you know, if I want to go into asset management, I have a real passion for it. I certainly don't discourage them from going to the U.S. markets. but by the same token, I say, you should examine other parts of the world because there may be more opportunity in parts of Asia or Africa or parts of Europe than there are in the United States. Through the course of this podcast, it's just a big discovery, serendipity engine. It's kind of how I

describe it. What I've learned is just how big the world is. So even in the U.S., there's 800,000 companies that are sort of relevant from a size standpoint. And when you look at the people, and I've talked to several of them, people like Brent Bishore, the folks from Chenmark Capital, who are buying very small U.S. businesses where if they go in and establish a simple email system or order flow system, all of a sudden margins expand by, you know, huge numbers and the prices are much cheaper. And so even if the scale isn't there, which obviously it isn't, these are much smaller companies, there seem to be for those interested in the business of investing, pockets everywhere, whether it's international microcap stocks or Asian equities where there's, you know, less less, less, uh, advanced systems or there's always seems to be something, right? There's always going to be some frontier at which you can earn returns higher than a simple S&P 500 fund. I realize, I want to be very clear that I realize how hard that is to find those things ahead of time and not just pile

into them after the fact. But as I was looking through your, there's a neat exhibit. It's exhibit 11 in the thing on competitive modes. And it breaks it down by industry structure. And I was trying to categorize asset management in here. And the two that seem to apply are either mature industry if you're being optimistic, declining industry, if you're being a little more pessimistic. And I thought I would just close by reading off, and I've got one more question for you, some of the opportunities to focus on in those two categories. So in mature, it's product refinement, investment in service quality, and process innovation. All those still seem very applicable in the asset management space. And then in the declining industry, it's this idea that

you just mentioned, niche strategy, divestment strategy, potentially, and then harvest strategy. So I was curious what the harvest strategy means. I think harvest just means don't keep plowing money back into a business, let it die a graceful death and collect cash as it comes out the door. And, you know, it's an interesting, I was watching, and I don't know if you had a vibe on this, but watching Buffett's discussion about IBM. And, you know, he sort of said it's a mistake, and they sold a bunch of stock at higher level. but presuming that the core business will not grow much or at all.

And, you know, they're trying to shift the basic orientation of the company, which is interesting. But here's the interesting sort of hypothetical question. It's a sort of counterfactual, which is, would you just be better off letting the core business melt like an ice cube and just return all the cash to shareholders and buybacks and dividends? Not even bother reinvesting. Now, as a CEO, it's not very inspiring. to say, I'm going to be overlooking a shrinking business. But in a sense, you're giving the money back to capital markets that can redeploy it and may be able to redeploy it more effectively than

you can. So I think that the harvest thing is to say there is no pride and should have no pride about just giving money back, managing the business as well as you can, as thoughtfully as you can, but recognizing that 10 years or 20 years from now will be much smaller than it is today. So I'm going to try to start asking people towards the end, especially people I've had on a few times to describe sort of the resources that they've come across recently that they find fascinating. It doesn't have to be an investing at all. It could be books, movies, documentaries, anything that's really kind of piqued your interest and made you think in the six months since we last talked. Well, Patrick, you know for me it's going to be books usually, right? And so I'll just

mention a couple things that I've read and liked a lot. I was late to this, but I just recently read Cal Newport's book, Deep Work. So good. So, so good, right? And, timely. And timely, right? So that's just really this idea that you can gain a competitive advantage in a cognitive world by doing so-called deep work, working on difficult cognitive problems that really require fundamental concentration and that much of our world has gotten very superficial and so forth. So there's a real advantage to so-called deep work versus shallow work. So I like that a lot. I've read two investment books that I actually liked a lot. And the first is The End of Theory by Rick Bookstabber. So Bookstabber is a, he He wrote Demons of Our Own Design, 2007, preceding the financial crisis. Very interesting guy, just by way of background. The end of theory, actually, the title sort of says it all. He sort of says economic theory and finance theory by extension really doesn't capture the irreducible uncertainty in the world.

So he's a big advocate for agent-based models and all the things that agent-based models bring along with them. So it's this idea of opening up your mind, understanding the world are complex systems. Agent-based models might be better ways to think about. that. He's got, I think, in there really, to me, at least compelling discussion about liquidity. So that's, I guess it was reinforcing my view on that and learning about it as well. So End of Theory is a really nice book. And I just finished this week, a book I also really liked was Andrew Lowe's new book called Adaptive Markets. And I would just say even for young people, it's a great book in the sense that he gives you,

by the way, Andrew Lowe is like a legit, you know, he's got street credit as a financial economist, right? And he wrote this sort of seminal paper in the mid-1980s called it, you know, that markets are not random walks. And they use some, at the time, at least fairly sophisticated econometric ways to demonstrate that. But this book is really nice because it actually gives you the foundation of efficient markets. And that's really important for everybody to understand is a touchdown for everything. But then he has forays into, you know, neuroscience and psychology into evolutionary theory and ties all these things back to how they apply to markets. And, you know, the adaptive market hypothesis is very much akin to the work we do

the Santa Fe Institute is thinking about markets as complex adaptive systems, right? Complex means lots of interaction agents. Adaptive means that they, the rules evolve, so their evolution of decision rules, and system being the part, the whole is more complex in the underlying pieces. So that book, I thought, was really terrific. And, you know, we didn't delve into it in great detail, but we mentioned is this man-machine thing. And so the book I'm reading now, I'm only about halfway through, but it's a great one, is Gary Kasparov's new book called Deep Thinking. And this is really, Kasparov's thoughts about, first of all, his interaction and, you know, sort of a story behind the match against Deep Blue back in 1997, but what's happened in the subsequent 20 years.

And, you know, again, very thoughtful guy, and it's got a lot of relevance for thinking about man, machine, how we think about what's, who's, what, entities good at what and so forth. So those are, those are a few of the things that I've had a lot of fun with just recently, and I I'd recommend all those things highly. Well, if you write a quarter as much in the next six months as you did in the last six, it'll be a good excuse to have you back again towards the end of the year. So as always, thank you very much. This has been awesome.

A good measure of a podcast for me is the fact that people won't be able to listen to this one at two-time speed. This is a one-timeser. So thank you very much. This has been a blast. My pleasure. Thanks, Patrick. Hey, everyone. Patrick here again.

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